

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__snake_case				:      str   		=			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__snake_case				:      Tuple   		=			{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__snake_case				:      List[str]   		=			{
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__snake_case				:      List[Any]   		=			{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Any       					=			VOCAB_FILES_NAMES
						_A    :				Union[str, Any]       					=			PRETRAINED_VOCAB_FILES_MAP
						_A    :				Dict       					=			PRETRAINED_INIT_CONFIGURATION
						_A    :				List[str]       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A    :				Optional[Any]       					=			LxmertTokenizer





						def __init__(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase="[UNK]"  ,      _lowercase="[SEP]"  ,      _lowercase="[PAD]"  ,      _lowercase="[CLS]"  ,      _lowercase="[MASK]"  ,      _lowercase=True  ,      _lowercase=None  ,      **_lowercase  ,      )     -> Union[str, Any]:
									super().__init__(
									    _lowercase  ,      tokenizer_file=_lowercase  ,      do_lower_case=_lowercase  ,      unk_token=_lowercase  ,      sep_token=_lowercase  ,      pad_token=_lowercase  ,      cls_token=_lowercase  ,      mask_token=_lowercase  ,      tokenize_chinese_chars=_lowercase  ,      strip_accents=_lowercase  ,      **_lowercase  ,      )

									a_						:						List[str] 							= json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
									if (
									    normalizer_state.get("""lowercase"""  ,      _lowercase    ) != do_lower_case
									    or normalizer_state.get("""strip_accents"""  ,      _lowercase    ) != strip_accents
									    or normalizer_state.get("""handle_chinese_chars"""  ,      _lowercase    ) != tokenize_chinese_chars
									):
												a_						:						Union[str, Any] 							= getattr(_lowercase  ,      normalizer_state.pop("""type"""    )    )
												a_						:						List[Any] 							= do_lower_case
												a_						:						List[Any] 							= strip_accents
												a_						:						List[str] 							= tokenize_chinese_chars
												a_						:						List[str] 							= normalizer_class(**_lowercase    )

									a_						:						int 							= do_lower_case





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase=None    )     -> Dict:
									a_						:						Any 							= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

									return output





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> List[int]:
									a_						:						List[Any] 							= [self.sep_token_id]
									a_						:						Dict 							= [self.cls_token_id]
									if token_ids_a is None:
												return len(cls + token_ids_a + sep    ) * [0]
									return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> Tuple[str]:
									a_						:						List[Any] 							= self._tokenizer.model.save(_lowercase  ,      name=_lowercase    )
									return tuple(_lowercase    )





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


